Research Papers

Farmatics

Plant diseases are one of the leading causes of agricultural losses worldwide, annually destroying an estimated 20–40% of global crop production and threatening the food security of billions. Early and accurate identification of plant diseases is critical to enabling timely intervention and minimizing crop losses. Farmatics is a deep learning-based plant disease detection system specifically designed to assist farmers in identifying diseases at an early stage across multiple crop varieties directly from leaf photographs captured via a mobile or web interface. The system leverages a custom Convolutional Neural Network (CNN) architecture trained on the PlantVillage benchmark dataset comprising 54,306 images across 38 disease classes and 14 crop varieties. Unlike existing solutions that are limited to single-crop detection, require constant internet connectivity, or lack early-stage detection capability, Farmatics supports multi-crop inference and is optimized for practical agricultural deployment. Experimental evaluation demonstrates that the proposed system achieves 96.4% classification accuracy, outperforming established baselines including VGG16 (91.2%), ResNet50 (92.5%), MobileNetV2 (89.7%), and InceptionV3 (93.1%). The system further provides confidence-scored disease identification, crop-specific treatment recommendations, and a confidence thresholding mechanism for quality assurance. Farmatics represents a significant step toward democratizing precision agricultural technology for smallholder farmers in India and comparable developing economies. Index Terms — Plant Disease Detection, Convolutional Neural Network, Deep Learning, Precision Agriculture, Image Classification, Early Detection, Multi-Crop Analysis, PlantVillage, Transfer Learning, Computer Vision.

By Ayush Santosh Naik · Vishwaniketan Institute ,khalapur